Determining ore characteristics

ABSTRACT

Techniques for processing ore include the steps of causing an imaging capture system to record a plurality of images of a stream of ore fragments en route from a first location in an ore processing facility to a second location in the ore processing facility; correlating the plurality of images of the stream of ore fragments with at least one or more characteristics of the ore fragments using a machine learning model that includes a plurality of ore parameter measurements associated with the one or more characteristics of the ore fragments; determining, based on the correlation, at least one of the one or more characteristics of the ore fragments; and generating, for display on a user computing device, data indicating the one or more characteristics of the ore fragments or data indicating an action or decision based on the one or more characteristics of the ore fragments.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/892,861, filed on Jun. 4, 2020, which claims priority under 35 U.S.C.§ 119 to U.S. Provisional Patent Application Ser. No. 62/857,592, filedon Jun. 5, 2019, the entire contents of which are incorporated byreference herein.

TECHNICAL FIELD

This disclosure generally relates to determining ore characteristicsand, more particularly, determining ore characteristics with one or moremachine learning models.

BACKGROUND

Ore is processed through a complex series of steps such as, for example,comminution, sizing, concentration (such as froth flotation),electrostatic or magnetic separation. There are many variables that canbe adjusted to improve the processing, based on the characteristics ofthe ore being processed.

SUMMARY

In general, the disclosure relates to using an imaging capture system torecord video, or a series of still images of ore fragments as thefragments are en route from one location to another in an ore processingfacility. The images of ore fragments are then used as input to amachine learning model, which can correlate characteristics of the orein the images with one or more parameter measurements associated withthe characteristics of the ore, and determine the characteristics of theore that is being imaged. These determined characteristics can then bereadily displayed to a user on a computing device, or used as input tochange an operating parameter or mode of operation of the processingfacility. In one implementation the system can detect anomalous piecesof ore, and stop a conveyor belt, to allow for further inspection, or toprevent damage to equipment.

In general, innovative aspects of the subject matter described in thisspecification can be embodied in methods that include the actions ofusing a machine learning model to determine characteristics of ore in anore processing facility. Other implementations of this aspect includecorresponding systems, apparatus, and computer programs, configured toperform the actions of the methods, encoded on computer storage devices.

In an example implementation, an ore processing system includes one ormore processors; one or more tangible, non-transitory media operablyconnectable to the one or more processors and storing instructions that,when executed, cause the one or more processors to perform operations.The operations include causing an imaging capture system to record aplurality of images of a stream of ore fragments en route from a firstlocation in an ore processing facility to a second location in the oreprocessing facility; correlating the plurality of images of the streamof ore fragments with one or more characteristics of the ore fragmentsusing a machine learning model that includes a plurality of oreparameter measurements associated with the one or more characteristicsof the ore fragments; determining, based on the correlation, at leastone of the one or more characteristics of the ore fragments; andgenerating, for display on a user computing device, data indicating theone or more characteristics of the ore fragments or data indicating anaction or decision based on the one or more characteristics of the orefragments.

In an aspect combinable with the example implementation, the pluralityof images include images including layers of red, green, blue, and grey;hyperspectral images; acoustic images; gravimetric images; or depthimagery images.

In an aspect combinable with any one of the previous aspects, themachine learning model includes an artificial neural network.

In an aspect combinable with any one of the previous aspects, theplurality of ore parameter measurements include measurements based on atleast one of x-ray diffraction (XRD), x-ray fluorescence (XRF), orenergy dispersive x-ray (EDS).

In an aspect combinable with any one of the previous aspects, the one ormore characteristics includes at least one of mineral composition,density, porosity, fracture type, fragment size, fragment moisturecontent, or hardness.

In an aspect combinable with any one of the previous aspects, theoperations further include based on the determined one or morecharacteristics of the ore fragments, adjusting an operation of the oreprocessing facility.

In an aspect combinable with any one of the previous aspects, adjustingan operation of the ore processing facility includes at least one ofcausing a change of route of the stream of ore fragments from the firstlocation in the ore processing facility to a third location in the oreprocessing facility different than the second location; causing a changeto an ore processing parameter in the ore processing facility; orcausing an adjustment of an ore source of the stream of ore fragmentsmoving through the ore processing facility.

In an aspect combinable with any one of the previous aspects, causing achange to an ore processing parameter in the ore processing facilityincludes causing a change to a chemical composition of a froth flotationsystem of the ore processing facility.

An aspect combinable with any one of the previous aspects furtherincludes an electromagnetic (EM) imaging system.

In an aspect combinable with any one of the previous aspects, theoperations further include causing the EM imaging system to record aplurality of EM images of the stream of ore fragments moving from thefirst location in the ore processing facility to the second location inthe ore processing facility; and determining, based on the plurality ofEM images, one or more mineral characteristics of the ore fragments.

In an aspect combinable with any one of the previous aspects, the one ormore mineral characteristics includes at least one of ore fragmentdensity, ore fragment size, or ore fragment surface composition.

In an aspect combinable with any one of the previous aspects, theoperations further include determining, based on at least one of theplurality of images, an anomaly within the stream of ore fragments; andbased on the determination of the anomaly, causing a change to anoperation of the ore processing facility.

In an aspect combinable with any one of the previous aspects, the changeto the operation of the ore processing facility includes causing a stopto a movement of the stream of ore fragments en route from the firstlocation in the ore processing facility to the second location in theore processing facility.

In an aspect combinable with any one of the previous aspects, causingthe imaging capture system to record the plurality of images of thestream of ore fragments en route from the first location in the oreprocessing facility to the second location in the ore processingfacility includes causing the imaging capture system to record theplurality of images of the stream of ore fragments as the ore fragmentsare moving from the first location in the ore processing facility to thesecond location in the ore processing facility.

In an aspect combinable with any one of the previous aspects, causingthe imaging capture system to record the plurality of images of thestream of ore fragments as the ore fragments are moving from the firstlocation in the ore processing facility to the second location in theore processing facility includes causing the imaging capture system torecord the plurality of images of the stream of ore fragments as the orefragments are moving on a conveyor or belt continuous feed system fromthe first location in the ore processing facility to the second locationin the ore processing facility.

In an aspect combinable with any one of the previous aspects, themachine learning model is trained on a data corpus that includes aplurality of ore fragment samples measured by at least one of x-raydiffraction (XRD), x-ray fluorescence (XRF), or energy dispersive x-ray(EDS) to correlate a plurality of ore parameter measurements of the orefragment samples with at least one ore fragment characteristic of theore fragment samples.

In an aspect combinable with any one of the previous aspects, thetrained machine learning model is retrainable based on at least one of:a change in source location of the ore fragments in the stream of orefragments, or a change in geological location of the ore fragments inthe stream of ore fragments.

In an aspect combinable with any one of the previous aspects, the orefragments are pretreated with an imaging enhancement prior to therecording of the plurality of images.

In another example implementation, a computer-implemented ore processingmethod executed by one or more processors includes causing an imagingcapture system to record a plurality of images of a stream of orefragments en route from a first location in an ore processing facilityto a second location in the ore processing facility; correlating theplurality of images of the stream of ore fragments with at least one ormore characteristics of the ore fragments using a machine learning modelthat includes a plurality of ore parameter measurements associated withthe one or more characteristics of the ore fragments; determining, basedon the correlation, at least one of the one or more characteristics ofthe ore fragments; and generating, for display on a user computingdevice, data indicating the one or more characteristics of the orefragments or data indicating an action or decision based on the one ormore characteristics of the ore fragments.

In an aspect combinable with the example implementation, the pluralityof images include images including layers of red, green, blue, and grey;hyperspectral images; acoustic images; gravimetric images; or depthimagery images.

In an aspect combinable with any one of the previous aspects, themachine learning model includes an artificial neural network.

In an aspect combinable with any one of the previous aspects, theplurality of ore parameter measurements include measurements based on atleast one of x-ray diffraction (XRD), x-ray fluorescence (XRF), orenergy dispersive x-ray (EDS).

In an aspect combinable with any one of the previous aspects, the one ormore characteristics includes at least one of mineral composition,density, porosity, or hardness.

An aspect combinable with any one of the previous aspects furtherincludes based on the determined one or more characteristics of the orefragments, adjusting an operation of the ore processing facility.

In an aspect combinable with any one of the previous aspects, adjustingan operation of the ore processing facility includes at least one ofcausing a change of route of the stream of ore fragments from the firstlocation in the ore processing facility to a third location in the oreprocessing facility different than the second location; causing a changeto an ore processing parameter in the ore processing facility; orcausing an adjustment of an ore source of the stream of ore fragmentsmoving through the ore processing facility.

In an aspect combinable with any one of the previous aspects, causing achange to an ore processing parameter in the ore processing facilityincludes causing a change to a chemical composition of a froth flotationsystem of the ore processing facility.

An aspect combinable with any one of the previous aspects furtherincludes causing an electromagnetic (EM) imaging system to record aplurality of EM images of the stream of ore fragments moving from thefirst location in the ore processing facility to the second location inthe ore processing facility; determining, based on the plurality of EMimages, one or more mineral characteristics of the ore fragments.

In an aspect combinable with any one of the previous aspects, the one ormore mineral characteristics includes at least one of ore fragmentdensity, ore fragment size, or ore fragment surface composition.

An aspect combinable with any one of the previous aspects furtherincludes determining, based on at least one of the plurality of images,an anomaly within the stream of ore fragments; and based on thedetermination of the anomaly, causing a change to an operation of theore processing facility.

In an aspect combinable with any one of the previous aspects, the changeto the operation of the ore processing facility includes causing a stopto movement of the ore stream en route from the first location in theore processing facility to the second location in the ore processingfacility.

In an aspect combinable with any one of the previous aspects, causingthe imaging capture system to record the plurality of images of thestream of ore fragments en route from the first location in the oreprocessing facility to the second location in the ore processingfacility includes causing the imaging capture system to record theplurality of images of the stream of ore fragments as the ore fragmentsare moving from the first location in the ore processing facility to thesecond location in the ore processing facility.

In an aspect combinable with any one of the previous aspects, causingthe imaging capture system to record the plurality of images of thestream of ore fragments as the ore fragments are moving from the firstlocation in the ore processing facility to the second location in theore processing facility includes causing the imaging capture system torecord the plurality of images of the stream of ore fragments as the orefragments are moving on a conveyor or belt continuous feed system fromthe first location in the ore processing facility to the second locationin the ore processing facility.

In an aspect combinable with any one of the previous aspects, themachine learning model is trained on a data corpus that includes aplurality of ore fragment samples measured by at least one of x-raydiffraction (XRD), x-ray fluorescence (XRF), or energy dispersive x-ray(EDS) to correlate a plurality of ore parameter measurements of the orefragment samples with at least one ore fragment characteristic of theore fragment samples.

In an aspect combinable with any one of the previous aspects, thetrained machine learning model is retrainable based on at least one of:a change in source location of the ore fragments in the stream of orefragments, or a change in geological location of the ore fragments inthe stream of ore fragments.

In an aspect combinable with any one of the previous aspects, the orefragments are pretreated with an imaging enhancement prior to therecording of the plurality of images.

In another example implementation, a non-transitory computer readablestorage medium storing instructions that, when executed by at least oneprocessor, cause the at least one processor to perform operations thatinclude causing an imaging capture system to record a plurality ofimages of a stream of ore fragments en route from a first location in anore processing facility to a second location in the ore processingfacility; correlating the plurality of images of the stream of orefragments with at least one or more characteristics of the ore fragmentsusing a machine learning model that includes a plurality of oreparameter measurements associated with the one or more characteristicsof the ore fragments; determining, based on the correlation, at leastone of the one or more characteristics of the ore fragments; andgenerating, for display on a user computing device, data indicating theone or more characteristics of the ore fragments or data indicating anaction or decision based on the one or more characteristics of the orefragments.

In another example implementation, an ore processing system includes oneor more processors; and one or more tangible, non-transitory mediaoperably connectable to the one or more processors and storinginstructions that, when executed, cause the one or more processors toperform operations. The operations include causing an optical imagingcapture system to record a plurality of images of a stream of orefragments en route from a first location in an ore processing facilityto a second location in the ore processing facility; correlating theplurality of images of the stream of ore fragments with at least one ormore characteristics of the ore fragments using a machine learning modelthat includes a plurality of ore parameter measurements associated withthe one or more characteristics of the ore fragments; determining, basedon the correlation, at least one of the one or more characteristics ofthe ore fragments; and generating, for display on a user computingdevice, data indicating the one or more characteristics of the orefragments or data indicating an action or decision based on the one ormore characteristics of the ore fragments.

The details of one or more implementations of the subject matter of thisdisclosure are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

DESCRIPTION OF DRAWINGS

FIG. 1A depicts an example implementation of at least a portion of anore processing facility that includes an ore composition imaging system.

FIG. 1B depicts another example implementation of at least a portion ofan ore processing facility that includes an ore composition imagingsystem.

FIG. 2 depicts a computing system with a machine learning model foranalyzing images of ore fragments.

FIG. 3 is a flowchart illustrating an example method for determiningcharacteristics of ore, and altering a processing facilities operationbased on the determination.

FIG. 4 is a flowchart illustrating an example method for training amachine learning system to determine ore characteristics in an orecomposition imaging system.

FIG. 5 depicts a computing system for an ore processing facility thatincludes an ore composition imaging system.

DETAILED DESCRIPTION

FIG. 1A depicts an example implementation of at least a portion of anore processing facility 100. The system 100 can be integrated into aconventional ore processing facility, or at a mining facility, or anyother suitable location. In a typical ore processing facility oretransported from the mine must be processed to separate valuableminerals. This process often includes comminution, or the reduction offragment size on the material. This is often done via crushing orgrinding. Then the ore is sorted according to size, and concentrated.Concentration refers to increasing the concentration of valuableminerals in a given stream of ore. Concentration can occur via severalmethods (e.g., gravity concentration, froth flotation, electrostaticseparation, or magnetic separation).

In one implementation the ore composition imaging system 100 isintegrated after the ore is initially crushed, while it is beingtransported for further processing. The ore composition imaging system100 can include an image capture system 102, which records video and/orstill images of ore fragments 110 as they travel between two locationsin an ore processing facility. The images from the image capture systemcan then be used as input to a computing system 108 that includes amachine learning model. The machine learning model can determine, usingmeasured parameters of the ore fragments 110 ore characteristics, whichcan be used to adjust or modify the operation of the processingfacility. The ore composition imaging system 100 can optionally includean illumination system 104, which can enhance the images captured by theimage capture system 102, and a treatment system 106, which can treatthe ore to further enhance the images.

The mineral composition imaging system 100 includes an image capturesystem 102, which captures image data of ore fragments 110 as theytravel past the image capture system 102. The image data can include,but is not limited to, optical image data, hyperspectral images, x-rayimages, acoustic images, electro-magnetic images, gravimetric images,electromagnetic (EM) images, or depth imagery images such as LIDAR,RADAR, or stereoscopic images.

For example, in one implementation the image capture system 102 captureshigh definition red, green, blue (RGB) video, which can be captured by acommercial video camera. In another implementation, the image capturesystem 102 can be an array of complementary metal oxide-semiconductor(CMOS) sensors, or charge-coupled devices (CCD's). In thisimplementation, the image capture system 102 can capture image dataassociated with images over a range of wavelengths broader than visiblelight, for example, 300 nm to 1000 nm. These images captured over abroad range of wavelengths can constitute hyperspectral images.

In some instances an illumination system 104 may be used to enhance theimages captured by the image capture system 102. The illumination system104 can emit visible light, ultra violet (UV) light, infra-red (IR)light, x-ray radiation, radio-frequency (RF) radiation, or anycombination thereof, as well as any other suitable radiation forimproving the image captured by the image capture system 102.

The image capture system 102 can then send image data to the computingsystem 108. The computing system 108 processes the image data todetermine at least one characteristic of the ore fragments 110. Thecomputing system 108 can include a machine learning model, for example aneural network, the machine learning model can be trained to detectpatterns based on the image data and determine characteristics of theore fragments 110. The machine learning model can do this based oncorrelating parameters measured in the ore fragments 110 and parametersof known previous measurements or ore. This provides the computingsystem 108 with the ability to perform accurate pattern recognition,automatically, or with minimal user input once the system is properlytrained. For example, the machine learning model can determine themineral composition, density, porosity, fracture type, fragment size,moisture content, surface composition, and hardness, among other things.

The ore fragments 110 are transported throughout the ore processingfacility as they undergo various processing steps. The ore can betransported via a number of techniques, such as conveyor belt,train/cart and rail, barges, or slurry pipelines. The transport system114 depicted in FIG. 1A is depicted as a conveyor belt; the presentdisclosure is not limiting thereto. The mineral composition imagingsystem 100 can be used in conjunction with any combination of transportsystem 114.

In one implementation, the mineral composition imaging system 100includes a treatment system 106. The treatment system 106 can spray theore fragments with a treatment solution prior to the ore being imaged.The treatment solution can enhance the captured images received by theimage capture system 102. The treatment can include, but is not limitedto, water, acid, non-penetrant dye, or a fluorescence enhancingsolution.

In an example operation of the mineral composition imaging system 100,ore fragments 110 may be, for example, places on a transport system 114after being comminuted and before sizing or concentration. The orefragments 110 may be brought to the transport system 114 from a separatefacility, such as a mining facility, or a comminution facility. The orefragments 110 can then be treated using the treatment system 106 whichcan spray the ore fragments 110 with, for example, an acid mixture, toremove external impurities and enhance the ore for imaging. Followingtreatment the ore passes by the image capture system 102, which recordsa series of images of the ore stream as the ore fragments 110 pass by.The images are then processed by a computing system 108 which correlatesparameters measured in the ore from the images with parameters containedin a machine learning model, and are associated with orecharacteristics. The computing system 108 can then determine at leastone characteristic about the ore passing by the image capture system102. For example, the mineral composition for the ore, among otherthings. Based on the output of the computing system 108 the oreprocessing facility can adjust a parameter to optimize the processing ofthe ore. In some examples, the ore processing facility may change theaddition rate of flotation reagents in a froth flotation processes, tocompensate for a change in mineral composition of the incoming orestream. Alternatively the processing facility may cause another changeto the chemical composition of the froth flotation system. In anotherinstance, the computing system 108 can detect an anomaly in thetransport system 114. For example, a tool or piece of equipment droppedonto a conveyor belt. In this instance the ore processing facility canstop the belts, preventing potential damage, or loss of equipment.

FIG. 1B depicts another example implementation of at least a portion ofan ore processing facility 100. The system 100 can be integrated into aconventional ore processing facility, or at a mining facility, or anyother suitable location. In a typical ore processing facility oretransported from the mine must be processed to separate valuableminerals. This process often includes comminution, or the reduction offragment size on the material. This is often done via crushing orgrinding. Then the ore is sorted according to size, and concentrated.Concentration refers to increasing the concentration of valuableminerals in a given stream of ore. Concentration can occur via severalmethods (e.g., gravity concentration, froth flotation, electrostaticseparation, magnetic separation)

In one implementation the ore composition imaging system 100 isintegrated after the ore is initially crushed, while it is beingtransported for further processing. The ore composition imaging system100 can include an image capture system 102, which records video and/orstill images of ore fragments 110 as they travel between two locationsin an ore processing facility. The images from the image capture systemcan then be used as input to a computing system 108 that includes amachine learning model. The machine learning model can determine, usingmeasured parameters of the ore fragments 110 ore characteristics, whichcan be used to adjust or modify the operation of the processingfacility. The ore composition imaging system 100 can optionally includean illumination system 104, which can enhance the images captured by theimage capture system 102, and a treatment system 106, which can treatthe ore to further enhance the images.

The mineral composition imaging system 100 includes an image capturesystem 102, which captures image data of ore fragments 110 as theytravel past the image capture system 102. The image data can include,but is not limited to, optical image data, hyperspectral images, x-rayimages, acoustic images, electro-magnetic images, gravimetric images,electromagnetic (EM) images, or depth imagery images such as LIDAR,RADAR, or stereoscopic images.

For example, in one implementation the image capture system 102 captureshigh definition red, green, blue (RGB) video, which can be captured by acommercial video camera. In another implementation, the image capturesystem 102 can be an array of complementary metal oxide-semiconductor(CMOS) sensors, or charge-coupled devices (CCD's). In thisimplementation, the image capture system 102 can capture image dataassociated with images over a range of wavelengths broader than visiblelight, for example, 300 nm to 1000 nm. These images captured over abroad range of wavelengths can constitute hyperspectral images.

In some instances an illumination system 104 may be used to enhance theimages captured by the image capture system 102. The illumination system104 can emit visible light, ultra violet (UV) light, infra-red (IR)light, x-ray radiation, radio-frequency (RF) radiation, or anycombination thereof, as well as any other suitable radiation forimproving the image captured by the image capture system 102.

The image capture system 102 can then send image data to the computingsystem 108. The computing system 108 processes the image data todetermine at least one characteristic of the ore fragments 110. Thecomputing system 108 can include a machine learning model, for example aneural network, the machine learning model can be trained to detectpatterns based on the image data and determine characteristics of theore fragments 110. The machine learning model can do this based oncorrelating parameters measured in the ore fragments 110 and parametersof known previous measurements or ore. This provides the computingsystem 108 with the ability to perform accurate pattern recognition,automatically, or with minimal user input once the system is properlytrained. For example, the machine learning model can determine themineral composition, density, porosity, fracture type, fragment size,moisture content, surface composition, and hardness, among other things.

The ore fragments 110 are transported throughout the ore processingfacility as they undergo various processing steps. The ore can betransported via a number of techniques, such as conveyor belt,train/cart and rail, barges, or slurry pipelines. The transport system114 depicted in FIG. 1B is shown as ore fragments being poured from onecart to another, or freefalling; the present disclosure is not limitingthereto. The mineral composition imaging system 100 can be used inconjunction with any combination of transport system 114.

In one implementation, the mineral composition imaging system 100includes a treatment system 106. The treatment system 106 can spray theore fragments with a treatment solution prior to the ore being imaged.The treatment solution can enhance the captured images received by theimage capture system 102. The treatment can include, but is not limitedto, water, acid, non-penetrant dye, or a fluorescence enhancingsolution.

In an example operation of the mineral composition imaging system 100,ore fragments 110 may be, for example, places on a transport system 114after being comminuted and before sizing or concentration. The orefragments 110 may be brought to the transport system 114 from a separatefacility, such as a mining facility, or a comminution facility. The orefragments 110 can then be treated using the treatment system 106 whichcan spray the ore fragments 110 with, for example, an acid mixture, toremove external impurities and enhance the ore for imaging. Followingtreatment the ore passes by the image capture system 102, which recordsa series of images of the ore stream as the ore fragments 110 pass by.The images are then processed by a computing system 108 which correlatesparameters measured in the ore from the images with parameters containedin a machine learning model, and are associated with orecharacteristics. The computing system 108 can then determine at leastone characteristic about the ore passing by the image capture system102. For example, the mineral composition for the ore, among otherthings. Based on the output of the computing system 108 the oreprocessing facility can adjust a parameter to optimize the processing ofthe ore. In one instance, the ore processing facility may change theaddition rate of flotation reagents in a froth flotation processes, tocompensate for a change in mineral composition of the incoming orestream. Alternatively the processing facility may cause another changeto the chemical composition of the froth flotation system. In anotherinstance, the computing system 108 can detect an anomaly in thetransport system 114. For example, a tool or piece of equipment droppedonto a conveyor belt. In this instance the ore processing facility canstop the belts, preventing potential damage, or loss of equipment.

FIG. 2 depicts an implementation of a computing system 108. In someimplementations, the machine learning model 204 (or portions thereof)can be executed by the image capture system 102. In some examples,operations of the machine learning model 204 can be distributed betweenthe image capture system 102 and the computing system 108.

The computing system 108 receives present data 202 from the imagecapture system 102 via the communications interface 212. The computingsystem 108 can also receive present data 202 from other user computingdevices 210, or a network. In some implementations the present data canbe received in real-time. The present data 202 is then used by themachine learning model 204 to generate an output determining one or morecharacteristics of the ore fragments 110. The present data 202 caninclude one of, or any combination of measured parameters of the orefragments 110. These measured parameters can be determined from, forexample, x-ray diffraction (XRD), x-ray fluorescence (XRF), energydispersive x-ray (EDS), and can be reflectivity, color, geometry, orimperfection density, among other things.

Characteristics of ore fragments 110 can include, but are not limitedto, mineral composition, density, porosity, fracture type, fragmentsize, fragment moisture content, surface composition, or hardness, amongother things.

The machine learning model 204 may also accept as input operationalparameters of the facility. These parameters can include, but are notlimited to, belt speed, ore transport rate, ore fragment 110 size, timeof day, or origin location of the ore. Facility parameters may beobtained via a manual input, or by additional sensors on the throughoutthe facility, among other things.

The computing system 108 can store in memory a historical data set 200for a particular facility. The historical data set can include all datathat has previously been used, or a subset of the previous data. Thehistorical data set 202 can also include data relating to common trendsseen across multiple facilities, among other things.

The machine learning model 204 receives the present data 202, and thehistorical data 200 and generates an output. For example, the machinelearning model 204 can compare the present data (e.g., present x-rayfluorescence) with historical data (e.g., historical x-ray fluorescencefor a known ore composition) to identify changes in the ore compositionas the ore fragments 110 pass the imaging capture system 102. Forexample, the machine learning model 204 can identify, a concentration ofa particular metal as it passes through the ore processing facility. Themachine learning model 204 can correlate the detected changes in the orefragment parameters with known patterns of ore fragment parameter (e.g.,a library of ore fragment images containing known characteristics) togenerate an output describing the ore fragments 110 passing through theore processing facility. The output can include, but is not limited to ameasurement of mineral composition, density, porosity, fracture type,fragment size, fragment moisture content, surface composition, orhardness, among other things.

Upon determining one or more characteristics of the ore fragments 110,the computing system 108 can provide a signal to alter one or moreoperations in the ore processing facility. In one implementation, thecomputing system 108 can simply provide for display on a user computingdevice 210, the determined characteristics. In another implementationthe computing system 108 can adjust the route of the ore fragments 110(e.g., activate a different belt or open a chute to a new hopper),allowing ore to be sent to a specific location in the processingfacility, to be processed. In yet another implementation, the computingsystem 108 can make an adjustment to a parameter in the ore processingfacility, for example, the computing system 108 can signal to increasethe addition rate of flotation reagents in a froth flotation processes,in response to determining the ore fragments 110 are changing density,or mineral composition. Additionally the computer system 108 can signalto change the speed of the transport system 114, or stop it altogether,in response to a detected characteristic in the ore fragments 110.

In some implementations, the machine learning model 204 incorporatesadditional data such as environmental factors associated with thefacility (e.g., weather, temperature, time of day, date, or location).For example, the machine learning model 204 can correlate the identifiedchanges in the ore fragments, with the environmental factors to assistin deterring one or more ore characteristics.

In some implementations, the machine learning model 204 is a deeplearning model that employs multiple layers of models to generate anoutput for a received input. A deep neural network is a deep machinelearning model that includes an output layer and one or more hiddenlayers that each apply a non-linear transformation to a received inputto generate an output. In some cases, the neural network may be arecurrent neural network. A recurrent neural network is a neural networkthat receives an input sequence and generates an output sequence fromthe input sequence. In particular, a recurrent neural network uses someor all of the internal state of the network after processing a previousinput in the input sequence to generate an output from the current inputin the input sequence. In some other implementations, the machinelearning model 204 is a convolutional neural network. In someimplementations, the machine learning model 204 is an ensemble of modelsthat may include all or a subset of the architectures described above.

In some implementations, the machine learning model 204 can be afeedforward autoencoder neural network. For example, the machinelearning model 204 can be a three-layer autoencoder neural network. Themachine learning model 204 may include an input layer, a hidden layer,and an output layer. In some implementations, the neural network has norecurrent connections between layers. Each layer of the neural networkmay be fully connected to the next, e.g., there may be no pruningbetween the layers. The neural network may include an optimizer fortraining the network and computing updated layer weights, such as, butnot limited to, ADAM, Adagrad, Adadelta, RMSprop, Stochastic GradientDescent (SGD), or SGD with momentum. In some implementations, the neuralnetwork may apply a mathematical transformation, e.g., a convolutionaltransformation or factor analysis to input data prior to feeding theinput data to the network.

In some implementations, the machine learning model 204 can be asupervised model. For example, for each input provided to the modelduring training, the machine learning model 204 can be instructed as towhat the correct output should be. The machine learning model 204 canuse batch training, e.g., training on a subset of examples before eachadjustment, instead of the entire available set of examples. This mayimprove the efficiency of training the model and may improve thegeneralizability of the model. The machine learning model 204 may usefolded cross-validation. For example, some fraction (the “fold”) of thedata available for training can be left out of training and used in alater testing phase to confirm how well the model generalizes. In someimplementations, the machine learning model 204 may be an unsupervisedmodel. For example, the model may adjust itself based on mathematicaldistances between examples rather than based on feedback on itsperformance.

A machine learning model 204 can be trained to recognize patterns in astream of ore fragments 110 when compared with the historical data,including images of ore fragments, and environmental parameters. In someexamples, the machine learning model 204 can be trained on hundreds ofrecorded images of ore fragments. The machine learning model 204 can betrained to identify specific characteristics of the ore fragments, orpotential anomalies in the fragment stream.

The machine learning model 204 can be, for example, a deep-learningneural network or a “very” deep-learning neural network. For example,the machine learning model 204 can be a convolutional neural network.The machine learning model 204 can be a recurrent network. The machinelearning model 204 can have residual connections or dense connections.The machine learning model 204 can be an ensemble of all or a subset ofthese architectures. The machine learning model 204 is trained todetermine one or more characteristics of the ore fragments 110 passingby the image capture system 102 based on detecting patterns from one ormore of the present data 202 and the historical data set 200. The modelmay be trained in a supervised or unsupervised manner. In some examples,the model may be trained in an adversarial manner. In some examples, themodel may be trained using multiple objectives, loss functions or tasks.

The machine learning model 204 can be configured to provide a binaryoutput, e.g., a yes or no indication of whether an anomaly is present inthe ore stream. In some examples, the machine learning model 204 isconfigured to determine multiple ore characteristics and a certaintyrating for each characteristic. For example, based on the present andhistorical data, the machine learning model can determine that orefragments have a 2% concentration of zinc, with a 70% certainty rating.

FIG. 3 is a flowchart illustrating an example method for determiningcharacteristics of ore, and altering a processing facilities operationbased on the determination. For clarity of presentation, the descriptionthat follows generally describes method 300 in the context of the otherfigures in this description. However, it will be understood that method300 can be performed, for example, by any system, environment, software,and hardware, or a combination of systems, environments, software, andhardware, as appropriate. In some implementations, various steps ofmethod 300 can be run in parallel, in combination, in loops, or in anyorder.

Method 300 can begin at 302. An image capture system 102 records imagesof a stream of ore fragments 110 as they travel between two locations inan ore processing facility. The images recorded can be, but are notlimited to optical RGB recordings, hyperspectral scans, x-ray images,electro-magnetic images, acoustic images, gravimetric images, or depthimagery images. In one implementation, the images are x-ray diffraction(XRD) images. In another implementation the images can be x-rayfluorescence (XRF), and can indicate the presence or concentrations ofspecific minerals in the ore. In yet another implementation the imagesare energy dispersive x-rays (EDS). The foregoing are exampleimplementations, the present disclosure is not limited thereto. From 302method 300 proceeds to 304.

At 304 the images of the stream of ore fragments are correlated with oneor more characteristics of the ore fragments. The correlation can beaccomplished using a machine learning model as described above. Themachine learning model can associate one or more parameter measurementsfrom the images with one or more characteristics of the ore fragmentsand correlate for example, ore mineral composition, density, porosity,fracture type, fragment size, fragment moisture content, surfacecomposition, or hardness, among other things. Method 300 then proceedsto 306.

At 306 the computing system 108 determines, based on the correlation,one or more characteristics of the ore fragments. These characteristicscan be the output of the machine learning model 204.

At 308 the one or more characteristics determined at 306 are displayedon a user computing device 210. The user computing device 210 can be alaptop, personal computer, cell phone, tablet, or any suitable devicewith a display. For example, the computing system 108 can determine themineral composition and fragment moisture content, and display thisinformation, along with a certainty measurement on a plant manager'scell phone, or a local control computer inside the ore processingfacility. The determined characteristics can be fed as an input toanother system as well.

At 310 a determination is made whether an operational change is requiredin the ore processing facility, based on the determined characteristics.If it is determined that a change is required, method 300 proceeds tostep 312. If it is determined that no change is required, method 300proceeds to step 314.

At 312, the operation of the ore processing facility is changed based onthe determined characteristics of the ore. For example, if it isdetermined that the total mass of the ore fragments has increased, beltspeed can be increased, and additional processing facilities can beactivated to accommodate the increased throughput. In another example,if it is determined that the mineral composition has changed, thetransport system 114 can be rerouted to a different processing system inthe ore processing facility. In yet another example, the ore processingfacility may change the addition rate of flotation reagents in a frothflotation processes, to compensate for a change in mineral compositionof the incoming ore stream. Alternatively the processing facility maycause a different change to the chemical composition of the frothflotation system. Following step 312 method 300 proceeds to 314.

At 314 a determination is made as to whether or not there is an anomalyin the stream of ore fragments 110. An anomaly can be, for example, atool or piece of equipment in the ore belt, or an oversize fragment,among other things. If it is determined that no anomaly is present,method 300 returns to 302, and continues normal operations. If it isdetermined that there is an anomaly present, method 300 proceeds to 316.

At 316 the computing system 108 can send a signal to stop the transportsystem 114 and cease movement of ore in the processing facility. Thiscan allow operators to investigate the anomaly or remove it asnecessary, preventing potential damage or loss of equipment. Followinginvestigation of the anomaly (and removal if necessary) the transportsystem 114 can be restarted, and method 300 returns to 302, resumingnormal operations.

FIG. 4 is a flowchart illustrating an example method for training amachine learning system to determine ore characteristics in an orecomposition imaging system. For clarity of presentation, the descriptionthat follows generally describes method 400 in the context of the otherfigures in this description. However, it will be understood that method400 can be performed, for example, by any system, environment, software,and hardware, or a combination of systems, environments, software, andhardware, as appropriate. In some implementations, various steps ofmethod 400 can be run in parallel, in combination, in loops, or in anyorder.

Method 400 begins at 402. A machine learning model is trained using adata corpus of XRD, XRF, or EDS images of ore fragments. The machinelearning model is then able to measure one or more parameters of the orefragments from the images. Ore fragments in the data corpus can have oneor more known characteristics, which can then be correlated by themachine learning model with the one or more measured parameters. Thetraining effectiveness can be determined using additional datacontaining images ore fragments with known characteristics, anddetermining if the machine learning model is able to accuratelydetermined the characteristics. Once it is determined that the machinelearning model is effectively able to correlate characteristics of theore with measured parameters, method 400 proceeds to step 404.

At step 404 a determination is made whether or not there has been achange in the ore fragments, and if retraining of the machine learningmodel is required. For example, if the ore fragments being imaged arefrom a new source location (e.g., different area in the mine, ordifferent geological layer), they may not have the same measurableparameters, or exhibit the same patterns as the data corpus used totrain the machine learning model. In another example, if the oreentering the ore processing facility may be from a different geologicallocation (e.g., a different mine, or a different type of ore,). If it isdetermined that there is a change in the ore fragments, or that themachine learning model needs to be retrained, method 400 proceeds tostep 406. If there has not been a change in the ore fragments, and themachine learning model does not need retraining, method 400 proceeds to408.

At 408 the machine learning model determines one or more characteristicsof the ore fragments. These determined characteristics can then bereadily displayed to a user on a computing device, or used as input tochange an operating parameter or mode of operation of the processingfacility. In one implementation the system can detect anomalous piecesof ore, and stop a conveyor belt, to allow for further inspection, or toprevent damage to equipment. The determined characteristics can be, butare not limited to the mineral composition, density, porosity, fracturetype, fragment size, moisture content, surface composition and hardness.

Returning to 404, if it was determined that the machine learning modelneeds to be retrained, method 400 proceeds to step 406. At 406, themachine learning model is retrained. Method 400 returns to step 402, andtraining begins again. The retraining of the machine learning model caninclude the original data corpus, or a new data corpus or a combinationthereof.

FIG. 5 is a schematic diagram of a computer system 500. The system 500can be used to carry out the operations described in association withany of the computer-implemented methods described previously, accordingto some implementations. In some implementations, computing systems anddevices and the functional operations described in this specificationcan be implemented in digital electronic circuitry, in tangibly-embodiedcomputer software or firmware, in computer hardware, including thestructures disclosed in this specification (e.g., computing system 108)and their structural equivalents, or in combinations of one or more ofthem. The system 500 is intended to include various forms of digitalcomputers, such as laptops, desktops, workstations, personal digitalassistants, servers, blade servers, mainframes, and other appropriatecomputers. The system 500 can also include mobile devices, such aspersonal digital assistants, cellular telephones, smartphones, and othersimilar computing devices. Additionally, the system can include portablestorage media, such as, Universal Serial Bus (USB) flash drives. Forexample, the USB flash drives may store operating systems and otherapplications. The USB flash drives can include input/output components,such as a wireless transducer or USB connector that may be inserted intoa USB port of another computing device.

The system 500 includes a processor 510, a memory 520, a storage device530, and an input/output device 540. Each of the components 510, 520,530, and 540 are interconnected using a system bus 550. The processor510 is capable of processing instructions for execution within thesystem 500. The processor may be designed using any of a number ofarchitectures. For example, the processor 510 may be a CISC (ComplexInstruction Set Computers) processor, a RISC (Reduced Instruction SetComputer) processor, or a MISC (Minimal Instruction Set Computer)processor.

In one implementation, the processor 510 is a single-threaded processor.In another implementation, the processor 510 is a multi-threadedprocessor. The processor 510 is capable of processing instructionsstored in the memory 520 or on the storage device 530 to displaygraphical information for a user interface on the input/output device540.

The memory 520 stores information within the system 500. In oneimplementation, the memory 520 is a computer-readable medium. In oneimplementation, the memory 520 is a volatile memory unit. In anotherimplementation, the memory 520 is a non-volatile memory unit.

The storage device 530 is capable of providing mass storage for thesystem 500. In one implementation, the storage device 530 is acomputer-readable medium. In various different implementations, thestorage device 530 may be a floppy disk device, a hard disk device, anoptical disk device, or a tape device.

The input/output device 540 provides input/output operations for thesystem 500. In one implementation, the input/output device 540 includesa keyboard and/or pointing device. In another implementation, theinput/output device 540 includes a display unit for displaying graphicaluser interfaces.

The features described can be implemented in digital electroniccircuitry, or in computer hardware, firmware, software, or incombinations of them. The apparatus can be implemented in a computerprogram product tangibly embodied in an information carrier, e.g., in amachine-readable storage device for execution by a programmableprocessor; and method steps can be performed by a programmable processorexecuting a program of instructions to perform functions of thedescribed implementations by operating on input data and generatingoutput. The described features can be implemented advantageously in oneor more computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that can be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program can be written in anyform of programming language, including compiled or interpretedlanguages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors ofany kind of computer. Generally, a processor will receive instructionsand data from a read-only memory or a random access memory or both. Theessential elements of a computer are a processor for executinginstructions and one or more memories for storing instructions and data.Generally, a computer will also include, or be operatively coupled tocommunicate with, one or more mass storage devices for storing datafiles; such devices include magnetic disks, such as internal hard disksand removable disks; magneto-optical disks; and optical disks. Storagedevices suitable for tangibly embodying computer program instructionsand data include all forms of non-volatile memory, including by way ofexample semiconductor memory devices, such as EPROM, EEPROM, and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,ASICs (application-specific integrated circuits). The machine learningmodel can run on Graphic Processing Units (GPUs) or custom machinelearning inference accelerator hardware.

To provide for interaction with a user, the features can be implementedon a computer having a display device such as a CRT (cathode ray tube)or LCD (liquid crystal display) monitor for displaying information tothe user and a keyboard and a pointing device such as a mouse or atrackball by which the user can provide input to the computer.Additionally, such activities can be implemented via touchscreenflat-panel displays and other appropriate mechanisms.

The features can be implemented in a computer system that includes aback-end component, such as a data server, or that includes a middlewarecomponent, such as an application server or an Internet server, or thatincludes a front-end component, such as a client computer having agraphical user interface or an Internet browser, or any combination ofthem. The components of the system can be connected by any form ormedium of digital data communication such as a communication network.Examples of communication networks include a local area network (“LAN”),a wide area network (“WAN”), peer-to-peer networks (having ad-hoc orstatic members), grid computing infrastructures, and the Internet.

The computer system can include clients and servers. A client and serverare generally remote from each other and typically interact through anetwork, such as the described one. The relationship of client andserver arises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular implementations of particularinventions. Certain features that are described in this specification inthe context of separate implementations can also be implemented incombination in a single implementation. Conversely, various featuresthat are described in the context of a single implementation can also beimplemented in multiple implementations separately or in any suitablesubcombination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

1.-36. (canceled)
 37. An ore processing system, comprising: one or moreprocessors; and one or more tangible, non-transitory media operablyconnectable to the one or more processors and storing instructions that,when executed, cause the one or more processors to perform operationscomprising: causing an imaging capture system to record a plurality ofimages of a stream of ore fragments en route from a first location in anore processing facility to a second location in the ore processingfacility; correlating the plurality of images of the stream of orefragments with one or more characteristics of the ore fragments using amachine learning model that comprises a plurality of ore parametermeasurements associated with the one or more characteristics of the orefragments; determining, based on the correlation, at least one of theone or more characteristics of the ore fragments; determining, based onat least one of the plurality of images, an anomaly within the stream ofore fragments; based on the determination of the anomaly, causing achange to an operation of the ore processing facility; and generating,for display on a user computing device, data indicating the one or morecharacteristics of the ore fragments or data indicating an action ordecision based on the one or more characteristics of the ore fragments.38. The system of claim 37, wherein the plurality of images comprise:images comprising layers of red, green, blue, and grey; hyperspectralimages; acoustic images; gravimetric images; or depth imagery images.39. The system of claim 37, wherein the plurality of ore parametermeasurements comprise measurements based on at least one of x-raydiffraction (XRD), x-ray fluorescence (XRF), or energy dispersive x-ray(EDS).
 40. The system of claim 37, wherein the one or morecharacteristics comprises at least one of mineral composition, density,porosity, fracture type, fragment size, fragment moisture content, orhardness.
 41. The system of claim 37, wherein causing a change to theoperation of the ore processing facility comprises at least one of:causing a change of route of the stream of ore fragments from the firstlocation in the ore processing facility to a third location in the oreprocessing facility different than the second location; causing a stopto a movement of the stream of ore fragments en route from the firstlocation in the ore processing facility to the second location in theore processing facility; or causing an adjustment of an ore source ofthe stream of ore fragments moving through the ore processing facility.42. The system of claim 37, further comprising an electromagnetic (EM)imaging system, the operations further comprising: causing the EMimaging system to record a plurality of EM images of the stream of orefragments moving from the first location in the ore processing facilityto the second location in the ore processing facility; and determining,based on the plurality of EM images, one or more mineral characteristicsof the ore fragments.
 43. The system of claim 42, wherein the one ormore mineral characteristics comprises at least one of ore fragmentdensity, ore fragment size, or ore fragment surface composition.
 44. Thesystem of claim 37, wherein causing the imaging capture system to recordthe plurality of images of the stream of ore fragments en route from thefirst location in the ore processing facility to the second location inthe ore processing facility comprises: causing the imaging capturesystem to record the plurality of images of the stream of ore fragmentsas the ore fragments are moving on a conveyor or belt continuous feedsystem from the first location in the ore processing facility to thesecond location in the ore processing facility.
 45. The system of claim37, wherein the machine learning model is trained on a data corpus thatcomprises a plurality of ore fragment samples measured by at least oneof x-ray diffraction (XRD), x-ray fluorescence (XRF), or energydispersive x-ray (EDS) to correlate a plurality of ore parametermeasurements of the ore fragment samples with at least one ore fragmentcharacteristic of the ore fragment samples.
 46. The system of claim 37,wherein the ore fragments are pretreated with an imaging enhancementprior to the recording of the plurality of images.
 47. Acomputer-implemented ore processing method executed by one or moreprocessors, the method comprising: causing an imaging capture system torecord a plurality of images of a stream of ore fragments en route froma first location in an ore processing facility to a second location inthe ore processing facility; correlating the plurality of images of thestream of ore fragments with one or more characteristics of the orefragments using a machine learning model that comprises a plurality ofore parameter measurements associated with the one or morecharacteristics of the ore fragments; determining, based on thecorrelation, at least one of the one or more characteristics of the orefragments; determining, based on at least one of the plurality ofimages, an anomaly within the stream of ore fragments; based on thedetermination of the anomaly, causing a change to an operation of theore processing facility; and generating, for display on a user computingdevice, data indicating the one or more characteristics of the orefragments or data indicating an action or decision based on the one ormore characteristics of the ore fragments.
 48. The method of claim 47,wherein the plurality of images comprise: images comprising layers ofred, green, blue, and grey; hyperspectral images; acoustic images;gravimetric images; or depth imagery images.
 49. The method of claim 47,wherein the plurality of ore parameter measurements comprisemeasurements based on at least one of x-ray diffraction (XRD), x-rayfluorescence (XRF), or energy dispersive x-ray (EDS).
 50. The method ofclaim 47, wherein the one or more characteristics comprises at least oneof mineral composition, density, porosity, fracture type, fragment size,fragment moisture content, or hardness.
 51. The method of claim 47,wherein causing a change to the operation of the ore processing facilitycomprises at least one of: causing a change of route of the stream ofore fragments from the first location in the ore processing facility toa third location in the ore processing facility different than thesecond location; causing a stop to a movement of the stream of orefragments en route from the first location in the ore processingfacility to the second location in the ore processing facility; orcausing an adjustment of an ore source of the stream of ore fragmentsmoving through the ore processing facility.
 52. The method of claim 47,further comprising an electromagnetic (EM) imaging system, theoperations further comprising: causing the EM imaging system to record aplurality of EM images of the stream of ore fragments moving from thefirst location in the ore processing facility to the second location inthe ore processing facility; and determining, based on the plurality ofEM images, one or more mineral characteristics of the ore fragments. 53.The method of claim 52, wherein the one or more mineral characteristicscomprises at least one of ore fragment density, ore fragment size, orore fragment surface composition.
 54. The method of claim 47, whereincausing the imaging capture system to record the plurality of images ofthe stream of ore fragments en route from the first location in the oreprocessing facility to the second location in the ore processingfacility comprises: causing the imaging capture system to record theplurality of images of the stream of ore fragments as the ore fragmentsare moving on a conveyor or belt continuous feed system from the firstlocation in the ore processing facility to the second location in theore processing facility.
 55. The method of claim 47, wherein the machinelearning model is trained on a data corpus that comprises a plurality ofore fragment samples measured by at least one of x-ray diffraction(XRD), x-ray fluorescence (XRF), or energy dispersive x-ray (EDS) tocorrelate a plurality of ore parameter measurements of the ore fragmentsamples with at least one ore fragment characteristic of the orefragment samples.
 56. A non-transitory computer readable storage mediumstoring instructions that, when executed by at least one processor,cause the at least one processor to perform operations comprising:causing an imaging capture system to record a plurality of images of astream of ore fragments en route from a first location in an oreprocessing facility to a second location in the ore processing facility;correlating the plurality of images of the stream of ore fragments withone or more characteristics of the ore fragments using a machinelearning model that comprises a plurality of ore parameter measurementsassociated with the one or more characteristics of the ore fragments;determining, based on the correlation, at least one of the one or morecharacteristics of the ore fragments; determining, based on at least oneof the plurality of images, an anomaly within the stream of orefragments; based on the determination of the anomaly, causing a changeto an operation of the ore processing facility; and generating, fordisplay on a user computing device, data indicating the one or morecharacteristics of the ore fragments or data indicating an action ordecision based on the one or more characteristics of the ore fragments.